Dynamic energy management with thermal comfort forecasting

Reducing greenhouse gas emissions and energy cost in the building sector largely relies on effective energy management. Yet, when it comes to heating or cooling, energy savings may translate to uncomfortable conditions for the building users. To ensure thermal comfort with a minimal energy consumption, in this paper we propose a modular methodology that dynamically schedules the operating hours of the heating/cooling system of the building using thermal comfort forecasts. To decide on the time and mode of operation, our approach utilizes indoor air temperature and relative humidity forecasting models, as well as a data-driven algorithm that predicts thermal comfort level based on said forecasts and employs pre-heating/cooling when necessary. Our empirical evaluation, conducted in the Casal Mira-sol culture center of Sant Cugat, Spain, suggests that our approach can improve the satisfaction of the building users, while achieving significant energy savings.

Automated summary

Reducing greenhouse gas emissions and energy cost in the building sector requires effective energy management. This paper proposes a modular methodology that schedules the operating hours of the building's heating/cooling system using thermal comfort forecasts to ensure minimal energy consumption. The approach utilizes indoor air temperature and relative humidity forecasting models, as well as a data-driven algorithm to predict and maintain thermal comfort levels. Empirical evaluation shows improved user satisfaction and significant energy savings.